The need and demand for an accurate, non-invasive method for determining attributes of tissue, other biological samples, or analyte concentrations in tissue or blood are well documented. For example, accurate non-invasive measurement of blood glucose levels in patients, particularly diabetics, would greatly improve treatment. Barnes et al. (U.S. Pat. No. 5,379,764) disclose the necessity for diabetics to frequently monitor glucose levels in their blood. It is further recognized that the more frequent the analysis, the less likely there will be large swings in glucose levels. These large swings are associated with the symptoms and complications of the disease, whose long-term effects can include heart disease, arteriosclerosis, blindness, stroke, hypertension, kidney failure, and premature death. As described below, several systems have been proposed for the non-invasive measurement of glucose in blood. However, despite these efforts, a lancet cut into the finger is still necessary for all presently commercially available forms of home glucose monitoring. This is believed so compromising to the diabetic patient that the most effective use of any form of diabetic management is rarely achieved.
The various proposed non-invasive methods for determining blood glucose level generally utilize quantitative infrared spectroscopy as a theoretical basis for analysis. In general, these methods involve probing glucose containing tissue using infrared radiation in absorption or attenuated total reflectance mode. Infrared spectroscopy measures the electromagnetic radiation (0.7–25 μm) a substance absorbs at various wavelengths. Atoms in molecules do not maintain fixed positions with respect to each other, but vibrate back and forth about an average distance. Absorption of light at the appropriate energy causes the molecules to become excited to a higher vibration level. The excitation of the molecules to an excited state occurs only at certain discrete energy levels, which are characteristic for that particular molecule. The most primary vibrational states occur in the mid-infrared frequency region (i.e., 2.5–25 μm). However, non-invasive analyte determination in blood in this region is problematic, if not impossible, due to the absorption of the light by water. The problem is overcome through the use of shorter wavelengths of light which are not as attenuated by water. Overtones of the primary vibrational states exist at shorter wavelengths and enable quantitative determinations at these wavelengths.
It is known that glucose absorbs at multiple frequencies in both the mid- and near-infrared range. There are, however, other infrared active analytes in the tissue and blood that also absorb at similar frequencies. Due to the overlapping nature of these absorption bands, no single or specific frequency can be used for reliable non-invasive glucose measurement. Analysis of spectral data for glucose measurement thus requires evaluation of many spectral intensities over a wide spectral range to achieve the sensitivity, precision, accuracy, and reliability necessary for quantitative determination. In addition to overlapping absorption bands, measurement of glucose is further complicated by the fact that glucose is a minor component by weight in blood and tissue, and that the resulting spectral data can exhibit a non-linear response due to both the properties of the substance being examined and/or inherent non-linearities in optical instrumentation.
A further common element to non-invasive glucose measuring techniques is the necessity for an optical interface between the body portion at the point of measurement and the sensor element of the analytical instrument. Generally, the sensor element can include an input element or means for irradiating the sample point with the infrared energy. The sensor element can further include an output element or means for measuring transmitted or reflected energy at various wavelengths resulting from irradiation through the input element. The optical interface also introduces variability into the non-invasive measurement.
Robinson et al. (U.S. Pat. No. 4,975,581) disclose a method and apparatus for measuring a characteristic of unknown value in a biological sample using infrared spectroscopy in conjunction with a multivariate model that is empirically derived from a set of spectra of biological samples of known characteristic values. The above-mentioned characteristic is generally the concentration of an analyte, such as glucose, but also can be any chemical or physical property of the sample. The method of Robinson et al. involves a two-step process that includes both calibration and prediction steps. In the calibration step, the infrared light is coupled to calibration samples of known characteristic values so that there is differential attenuation of at least several wavelengths of the infrared radiation as a function of the various components and analytes comprising the sample with known characteristic value. The infrared light is coupled to the sample by passing the light through the sample or by reflecting the light from the sample. Absorption of the infrared light by the sample causes intensity variations of the light that are a function of the wavelength of the light. The resulting intensity variations at the at least several wavelengths are measured for the set of calibration samples of known characteristic values. Original or transformed intensity variations are then empirically related to the known characteristic of the calibration samples using a multivariate algorithm to obtain a multivariate calibration model. In the prediction step, the infrared light is coupled to a sample of unknown characteristic value, and the calibration model is applied to the original or transformed intensity variations of the appropriate wavelengths of light measured from this unknown sample. The result of the prediction step is the estimated value of the characteristic of the unknown sample. The disclosure of Robinson et al. is incorporated herein by reference.
Barnes et al. (U.S. Pat. No. 5,379,764) disclose a spectrographic method for analyzing glucose concentration wherein near infrared radiation is projected on a portion of the body, the radiation including a plurality of wavelengths, followed by sensing the resulting radiation emitted from the portion of the body as affected by the absorption of the body. The method disclosed includes pretreating the resulting data to minimize influences of offset and drift to obtain an expression of the magnitude of the sensed radiation as modified.
Dähne et al. (U.S. Pat. No. 4,655,225) disclose the employment of near infrared spectroscopy for non-invasively transmitting optical energy in the near-infrared spectrum through a finger or earlobe of a subject. Also discussed is the use of near infrared energy diffusely reflected from deep within the tissues. Responses are derived at two different wavelengths to quantify glucose in the subject. One of the wavelengths is used to determine background absorption, while the other wavelength is used to determine glucose absorption.
Caro (U.S. Pat. No. 5,348,003) discloses the use of temporally modulated electromagnetic energy at multiple wavelengths as the irradiating light energy. The derived wavelength dependence of the optical absorption per unit path length is compared with a calibration model to derive concentrations of an analyte in the medium.
Wu et al. (U.S. Pat. No. 5,452,723) disclose a method of spectrographic analysis of a tissue sample which includes measuring the diffuse reflectance spectrum, as well as a second selected spectrum, such as fluorescence, and adjusting the spectrum with the reflectance spectrum. Wu et al. assert that this procedure reduces the sample-to-sample variability.
The intended benefit of using models such as those disclosed above, including multivariate analysis as disclosed by Robinson, is that direct measurements that are important but costly, time consuming, or difficult to obtain, can be replaced by other indirect measurements that are cheaper and easier to get. However, none of the prior art modeling methods, as disclosed, has proven to be sufficiently robust or accurate to be used as a surrogate or replacement for direct measurement of an analyte such as glucose.
Of particular importance to the present invention is the use of multivariate analysis. Measurement by multivariate analysis involves a two-step process. In the first step, calibration, a model is constructed utilizing a dataset obtained by concurrently making indirect measurements and direct measurements (e.g., by invasively drawing or taking and analyzing a biological sample such as blood for glucose levels) in a number of situations spanning a variety of physiological and instrumental conditions. A general form for the relationship between direct (blood-glucose concentration) and the indirect (optical) measurements is Ĝ=ƒ(y1, y2, . . . , yq), where Ĝ is the desired estimated value of the direct measurement (glucose), ƒ is some function (model), and y1, y2, . . . , yq (the arguments of ƒ) represents the indirect (optical) measurement, or transformed optical measurements, at q wavelengths. The goal of this first step is to develop a useful function, ƒ. In the second step, prediction, this function is evaluated at a measured set of indirect (optical) measurements {y1, y2, . . . , yq} in order to obtain an estimate of the direct measurement (blood-glucose concentration) at some time in the future when optical measurements will be made without a corresponding direct or invasive measurement.
Ideally, one would prefer to develop a calibration model that is applicable across all subjects and all instruments (i.e., instruments used to make the measurements). The ability to use a calibration developed on one instrument on another instrument is referred to as calibration transfer. The instrument or instruments that are used for collection of the calibration data are referred to as master instruments. Master instruments can be completely different instruments or an instrument(s) that are modified to produce different instrument conditions or states. The master instruments are used to produce calibration data which is typically composed of spectra and direct reference values. The calibration data can be used in raw form or processed in multiple ways to create calibration information. Calibration information can be simply the raw data, a calibration model, an eigenvector decomposition of the data, or any other suitable representation of the information content contained in the master calibration data. The calibration information is then used by a slave instrument such that the slave instrument can make prediction measurements. A slave instrument is simply an instrument that uses the master calibration information. In practice, the slave instrument is a production version of the master instruments. The slave instrument is manufactured to be the same as the master instrument, but variances in manufacturing result in measurable differences. The development of a single calibration model that works across these manufacturing differences is referred to as a universal model. A universal model or calibration is a calibration that can be transferred from the master instrument or instruments to the slave without adaptation, correction or other modifications. Universal models have been referred to as global calibration models in the literature. However, it has been shown that for many applications, subject and instrument variability make it difficult to develop a universal calibration model. Subject and instrument variability are specifically addressed in U.S. patent application Ser. No. 09/415,432, which has been incorporated by reference. The magnitude and general complexity of variation can be characterized by the standard deviation of the spectral data. FIG. 1 graphically illustrates the difference between inter-instrument variation and intra-instrument variation. The spectral data used to generate the figure was acquired over a six-week period and utilized 175 background measurements made on three different instruments. The inter-instrument variation is the standard deviation of the spectral data acquired over the time period. The intra-instrument variation was calculated by first meancentering the spectral data by instrument with subsequent calculation of the standard deviation. The spectral variation across instruments, inter-instrument spectral variation, is substantially larger than the intra-instrument variation and has a more complex spectral shape. The inter-instrument variation includes all spectral differences between the instruments, as well as the intra-instrument variations observed over the data acquisition period. Sources of spectral variation within an instrument include alignment changes, environmental changes, etc. The spectral variation across instruments is substantially larger than the sum of all effects within an instrument. Thus, the task of building a universal calibration model that will be effective across instruments is a daunting one.
Various attempts have been made to address instrument variability, but with limited success. For example, U.S. Pat. No. 4,866,644 to Shenk et al. teaches a method of developing an explicit correction for the spectra generated by each field instrument based upon the measurement of a common set of standard samples measured on the master and field instruments. U.S. Pat. No. 5,243,546 to Maggard teaches a method of developing an explicit correction to the calibration model for each field instrument based upon the measurement of a common set of standard samples measured on the master and field instruments. U.S. Pat. No. 5,459,677 to Kowalski et al. teaches a method of developing an explicit correction (“transfer coefficients”) for the spectra generated by each field (“target”) instrument based upon the measurement of a common set of standard samples measured on the master (“reference”) and field instruments. U.S. Pat. No. 5,552,997 to Massart teaches a method of developing and validating an explicit univariate calibration for each analytical instrument based upon the measurement of a set of standard samples with known reference values measured on the instrument of interest, allowing for changes in bias, slope and curvature. However, Massart does not address transfer of calibration, nor does Massart address a multivariate framework. U.S. Pat. No. 5,724,268 to Sodickson et al. teaches a method of estimating and compensating for spectral errors introduced by spectroscopic instrumentation by estimating and accounting for the error sources using least-squares or other mathematical estimation techniques.
A number of methods have also been proposed in the literature for transferring a calibration from one near-infrared spectrometer based instrument to another. These methods can be classified into four general categories: (1) pre-processing, (2) hybrid models, (3) wavelength selection, and (4) transformations. Methods within each category can be generally effective at compensating for certain instrument-to-instrument differences.
A pre-processing method is described in C. E. Anderson, J. H. Kalivas, “Fundamentals of Calibration Transfer Through Procrustes Analysis”, Appl. Spectros., 53(10), 1268 (1999). This method employs a statistical methodology called Procrustes analysis and, in particular, highlights a process they call translation. The authors conclude that “translation is the key step for transformation of spectra and can often be all that is required” to achieve calibration transfer. This technique can require a common set of samples to be measured on both the master and slave instruments.
Another pre-processing method called “orthogonal signal correction” is described by J. Sjoblom et al. in “An Evaluation of Orthogonal Signal Correction Applied to Calibration Transfer of Near Infrared Spectra”, Chemom & Intell Lab. Sys., 44, 229 (1998). This method can require a common set of samples to be measured on both the master and slave instruments and is reported to perform at about the same level as other known calibration transfer methods (piece-wise direct standardization and hybrid modeling).
Another pre-processing method wherein the derivative spectra are used for calibration and validation is compared to piece-wise direct standardization (PDS) in H. Swierenga et al., “Comparison of Two Different Approaches Toward Model Transferability in NIR Spectroscopy”, Appl. Spectros., 52(1), 7 (1998). It was reported that, in some cases, using derivative spectra was as effective as PDS, but in other cases, it performed poorly compared to PDS.
Hybrid modeling, wherein samples measured on both instruments are used directly in building the calibration, has been applied to a calibration transfer problem as described in D. Ozdemir et al., “Hybrid Calibration Models: An Alternative to Calibration Transfer”, Appl. Spectros., 52(4), 599 (1998). Results reportedly show that when using a multivariate analysis method such as partial least squares (PLS) to build a calibration, effective models should be constructed, but equal number of samples should be measured on both the master and slave instruments.
Wavelength selection, a method which attempts to identify and use only those wavelengths that contain information pertinent to the analyte of interest and minimize the inclusion of wavelengths that contain only instrument-specific data, has been applied to problems in calibration transfer. It has been reported by H. Swierenga et al. in “Improvement of PLS Model Transferability by Robust Wavelength Selection”, Chemom. Intell. Lab. Syst., 14, 237 (1998) that wavelength selection can perform calibration transfers as effectively as PDS.
Direct standardization and piece-wise direct standardization are methods used for calibration transfer that rely on the measurement of a number of standard samples on both the master and slave instruments. These methods are described by Y -D. Wang and B. R. Kowalski in “Calibration Transfer and Measurement Stability of Near-Infrared Spectrometers”, Appl. Spectros., 46(5), 764 (1992) and others (see, e.g., Y -D. Wang, M. J. Lysaght, B. R. Kowalski, “Improvement of Multivariate Calibration Through Instrument Standardization”, Anal. Chem., 64, 562 (1992); and Z. Wang, “Additive Background Correction in Multivariate Instrument Standardization”, Anal. Chem., 67, 2379 (1995)).
A technique called “optical matching” is reported by B. G. Osborne et al. in “Optical Matching of Near Infrared Reflectance Monochromator Instruments for the Analysis of Ground and Whole Wheat”, J. Near Infrared Spectrosc., 7, 167 (1999). This method again can require the use of a set of transfer samples measured on both instruments.
Techniques employing finite impulse response filters have been described by S. T. Sum and S. D. Brown in “Standardization of Fiber Optic Probes for Near-Infrared Multivariate Calibrations”, Appl. Spectros., 52(6), 869 (1998) and by T. B. Blanket al. in “Transfer of Near-infrared Multivariate Calibrations Without Standards”, Anal. Chem., 68, 2987 (1996). Although FIR filtering methods were generally found to be successful, this method was not as effective as PDS when a bias was present between the master and slave instruments.
In addition, it should be noted that efforts have been made to create calibration models that are robust to various instrumental changes that can occur after the calibration period. In “Strategy for Constructing Robust Multivariate Calibration Models”, Chemometrics and Intelligent Laboratory Systems, 49, 1–17 (1999), Swierenga et al. describe methods of assessing a calibration's sensitivity to environmental effects and apply various pre-processing techniques on the calibration set in order to reduce this sensitivity.
A method of selecting “robust variables”, resulting in a more robust calibration, is described by Swierenga et al. in “Development of Robust Calibration Models in Near Infra-Red Spectrometric Applications”, Anal. Chim. Acta, 411, 121–135 (2000). This work compares the effectiveness of selecting “robust variables” with the method of including the external variations in the calibration set.
Ozdemir et al. report in “Effect of Wavelength Drift on Single and Multi-instrument Calibration Using Genetic Regression”, Applied Spectroscopy, 52, 1203–1209 (1998) that, in simulation, inclusion of wavelength shifted spectra in the calibration serves to make the model more robust to wavelength shifts in the spectra of the validation set.
Near-infrared spectroscopy has been applied to many quantitative and qualitative analysis problems encountered in both academia and industry. Various techniques for creating a useful calibration model for a particular instrument have been proposed as discussed previously (for example, see Multivariate Calibration, H. Martens and T. Naes, 1989, Wiley and Sons Ltd.), but effective techniques for maintaining this calibration model on the same instrument across changes to the environment or instrument, or transferring the calibration model to another instrument have not been universally accepted.
The need for applying a single calibration model to multiple instruments arises in a variety of fields including, but not limited to, process and quality control. Applying a calibration model from one instrument to data collected on another (slave) instrument is made difficult by differences in instruments that give rise to a number of spectral effects (for example, instrument response, resolution, photometric accuracy, etc.). These differences will tend to introduce elevated errors in the predictions made by the slave instrument. These additional prediction errors can, in general, be classified as due to some combination of bias, slope, and precision. Bias errors are those that represent a fixed error, common to all predictions made on the slave instrument. Slope errors are those that are proportional to the magnitude of the attribute of biological tissue being measured, such as glucose concentration. Precision errors are calculated as the additional prediction error that is not ascribable to bias or slope.
In general, the process of creating a calibration model for a particular instrument is time consuming and expensive, and therefore impractical for applications requiring the use of multiple instruments or using a single instrument in different environments or with different sampling accessories. A method for transferring a calibration from one (master) instrument to another (slave) instrument (or multiple slave instruments) with minimal effort would be beneficial in a wide variety of fields employing near infrared spectroscopy.
Accordingly, the need exists for a method and apparatus for non-invasively measuring attributes of biological tissue, such as glucose concentrations in blood, which incorporates a model that is sufficiently robust to act as an accurate surrogate for direct measurement. The model would preferably account for instrument and subject variability. Specifically, the methods and apparatus should provide a model that eliminates or significantly reduces all forms of excess prediction error manifested as bias, slope or precision errors. In order to be commercially successful, applicants believe, the model should not require extensive sampling of the specific instrument and/or subject on which the model is to be applied in order to accurately predict a biological attribute such as glucose.
The present invention addresses these needs as well as other problems associated with existing models and calibrations used in methods for non-invasively measuring an attribute of a biological sample such as glucose concentration in blood. The present invention also offers further advantages over the prior art and solves problems associated therewith.